QSAR studies and molecular docking analysis of 1,4-dihydropyridines as Calcium Channel Blockers using DFT and ANN method

Abstract

Artificial neural networks (ANN) are very useful for predicting biological activities in QSAR studies. ANNs allow the study of complex and nonlinear SAR. We use ANN and MLR methods to generate QSAR models for Calcium Channel Blockers activity of a series of 1,4-dihydropyridines. Molecular descriptors were calculated by using DFT method at the B3LYP/6-31G+ (d, p) level. Statistical analyzes show that the predicted values of the activities are in excellent agreement with the experimental results. Molecular docking studies have been performed, in order to re-estimate the activity of molecules as CCBs by analyzing their binding energies and mutual interaction types.